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4 / 13 Autonomic option Autonomic computing Named after autonomic nervous system Systems can manage themselves according to an administrator’s goals Self-governing operation of the entire system, not just parts of it New components integrate as effortlessly as a new cell establishes itself in the body First step Examine the vision of autonomic computing

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11 / 13 Engineering challenges (3/3) Systemwide issues Authentication, encryption, signing Autonomic elements can identify themselves Autonomic system must be robust against insidious forms of attack Goal specification Humans provide the goal and constraints The indirect effect of policies Ensure that goals are specified correctly in the first place Autonomic systems will need to protect themselves from input goals that are inconsistent, implausible, dangerous, or unrealizable

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12 / 13 Scientific challenges Behavioral abstractions and models Mapping from local behavior to global behavior is a necessary Inverse relationship Robustness theory Learning and optimization theory Agents continually adapt to their environment that consists of other agents There are no guarantees of convergence Negotiation theory Automated statistical modeling

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13 / 13 Autonomic computing systems manage themselves according an administrator’s goals. We believe that it is possible to meet the grand challenge of autonomic computing without magic and without fully solving the AI problem. Conclusion